BotANNIC: Autonomous Robotic Platform Elevates Crop Monitoring and Fruit Quality Assessment
NUST MISIS researchers, in collaboration with Tambov State Technical University, China University of Mining Technology, and the Higher School of Economics, introduced the botANNIC robotic platform. This autonomous system traverses garden environments to detect diseased and damaged fruits. The announcement was reported by News, a publication that cites university experts about the practical implications for agriculture.
The platform uses a stereo camera fed by neural networks that imitate human vision. It surveys both deciduous and productive tree sections, locates apples within the canopy, and evaluates their maturity and structural integrity. Ivan Ushakov, head of the NUST MISIS Physics Department, outlined how botANNIC analyzes the fruiting climate and the factors influencing fruit quality, providing growers with actionable data for timely decision-making.
Alexander Divin, a professor in the Department of Mechatronics and Technological Measurements at Tambov State Technical University, reported that botANNIC achieves a classification accuracy of at least 80 percent under typical orchard conditions. This level of precision helps farmers distinguish between fruit-ready harvests and fruit that requires further attention, contributing to better resource management and reduced waste.
Previous work from MIT highlighted a versatile robotic arm capable of adaptable manipulation, a reference point frequently cited in discussions of progress in agricultural robotics. That lineage of innovation underscores the move toward autonomous systems that can operate in real farm environments with minimal human intervention.
MISIS News and institutional communications summarize the collaboration and its goals, emphasizing concrete gains in crop monitoring and fruit quality assessment. The project is framed as a step toward smarter agriculture through autonomous sensing and machine vision, intended to support both growers and researchers in optimizing harvest timing, resource use, and crop health monitoring.